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<div class="highlight"><pre><span></span><span class="ch">#!/usr/bin/python</span>
<span class="c1"># The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt</span>
<span class="c1">#</span>
<span class="c1">#   This example program shows how to find frontal human faces in an image and</span>
<span class="c1">#   estimate their pose.  The pose takes the form of 68 landmarks.  These are</span>
<span class="c1">#   points on the face such as the corners of the mouth, along the eyebrows, on</span>
<span class="c1">#   the eyes, and so forth.</span>
<span class="c1">#</span>
<span class="c1">#   The face detector we use is made using the classic Histogram of Oriented</span>
<span class="c1">#   Gradients (HOG) feature combined with a linear classifier, an image pyramid,</span>
<span class="c1">#   and sliding window detection scheme.  The pose estimator was created by</span>
<span class="c1">#   using dlib&#39;s implementation of the paper:</span>
<span class="c1">#      One Millisecond Face Alignment with an Ensemble of Regression Trees by</span>
<span class="c1">#      Vahid Kazemi and Josephine Sullivan, CVPR 2014</span>
<span class="c1">#   and was trained on the iBUG 300-W face landmark dataset (see</span>
<span class="c1">#   https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/):  </span>
<span class="c1">#      C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic. </span>
<span class="c1">#      300 faces In-the-wild challenge: Database and results. </span>
<span class="c1">#      Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation &quot;In-The-Wild&quot;. 2016.</span>
<span class="c1">#   You can get the trained model file from:</span>
<span class="c1">#   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2.</span>
<span class="c1">#   Note that the license for the iBUG 300-W dataset excludes commercial use.</span>
<span class="c1">#   So you should contact Imperial College London to find out if it&#39;s OK for</span>
<span class="c1">#   you to use this model file in a commercial product.</span>
<span class="c1">#</span>
<span class="c1">#</span>
<span class="c1">#   Also, note that you can train your own models using dlib&#39;s machine learning</span>
<span class="c1">#   tools. See <a href="train_shape_predictor.py.html">train_shape_predictor.py</a> to see an example.</span>
<span class="c1">#</span>
<span class="c1">#</span>
<span class="c1"># COMPILING/INSTALLING THE DLIB PYTHON INTERFACE</span>
<span class="c1">#   You can install dlib using the command:</span>
<span class="c1">#       pip install dlib</span>
<span class="c1">#</span>
<span class="c1">#   Alternatively, if you want to compile dlib yourself then go into the dlib</span>
<span class="c1">#   root folder and run:</span>
<span class="c1">#       python setup.py install</span>
<span class="c1">#</span>
<span class="c1">#   Compiling dlib should work on any operating system so long as you have</span>
<span class="c1">#   CMake installed.  On Ubuntu, this can be done easily by running the</span>
<span class="c1">#   command:</span>
<span class="c1">#       sudo apt-get install cmake</span>
<span class="c1">#</span>
<span class="c1">#   Also note that this example requires Numpy which can be installed</span>
<span class="c1">#   via the command:</span>
<span class="c1">#       pip install numpy</span>

<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">dlib</span>
<span class="kn">import</span> <span class="nn">glob</span>

<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
    <span class="k">print</span><span class="p">(</span>
        <span class="s2">&quot;Give the path to the trained shape predictor model as the first &quot;</span>
        <span class="s2">&quot;argument and then the directory containing the facial images.</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="s2">&quot;For example, if you are in the python_examples folder then &quot;</span>
        <span class="s2">&quot;execute this program by running:</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="s2">&quot;    ./<a href="face_landmark_detection.py.html">face_landmark_detection.py</a> shape_predictor_68_face_landmarks.dat ../examples/faces</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="s2">&quot;You can download a trained facial shape predictor from:</span><span class="se">\n</span><span class="s2">&quot;</span>
        <span class="s2">&quot;    http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2&quot;</span><span class="p">)</span>
    <span class="nb">exit</span><span class="p">()</span>

<span class="n">predictor_path</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">faces_folder_path</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>

<span class="n">detector</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">get_frontal_face_detector</span><span class="p">()</span>
<span class="n">predictor</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">shape_predictor</span><span class="p">(</span><span class="n">predictor_path</span><span class="p">)</span>
<span class="n">win</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">image_window</span><span class="p">()</span>

<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">faces_folder_path</span><span class="p">,</span> <span class="s2">&quot;*.jpg&quot;</span><span class="p">)):</span>
    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Processing file: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">load_rgb_image</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

    <span class="n">win</span><span class="o">.</span><span class="n">clear_overlay</span><span class="p">()</span>
    <span class="n">win</span><span class="o">.</span><span class="n">set_image</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>

    <span class="c1"># Ask the detector to find the bounding boxes of each face. The 1 in the</span>
    <span class="c1"># second argument indicates that we should upsample the image 1 time. This</span>
    <span class="c1"># will make everything bigger and allow us to detect more faces.</span>
    <span class="n">dets</span> <span class="o">=</span> <span class="n">detector</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Number of faces detected: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dets</span><span class="p">)))</span>
    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dets</span><span class="p">):</span>
        <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Detection {}: Left: {} Top: {} Right: {} Bottom: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">k</span><span class="p">,</span> <span class="n">d</span><span class="o">.</span><span class="n">left</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">top</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">right</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">bottom</span><span class="p">()))</span>
        <span class="c1"># Get the landmarks/parts for the face in box d.</span>
        <span class="n">shape</span> <span class="o">=</span> <span class="n">predictor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
        <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Part 0: {}, Part 1: {} ...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">part</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
                                                  <span class="n">shape</span><span class="o">.</span><span class="n">part</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>
        <span class="c1"># Draw the face landmarks on the screen.</span>
        <span class="n">win</span><span class="o">.</span><span class="n">add_overlay</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>

    <span class="n">win</span><span class="o">.</span><span class="n">add_overlay</span><span class="p">(</span><span class="n">dets</span><span class="p">)</span>
    <span class="n">dlib</span><span class="o">.</span><span class="n">hit_enter_to_continue</span><span class="p">()</span>
</pre></div>
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